Class09miniprojectinclass
San Luc (PID: A59010657)
10/29/2021
Importing candy data
candy_file <- "candy-data.csv"
candy = read.csv(candy_file, row.names=1)
head(candy)## chocolate fruity caramel peanutyalmondy nougat crispedricewafer
## 100 Grand 1 0 1 0 0 1
## 3 Musketeers 1 0 0 0 1 0
## One dime 0 0 0 0 0 0
## One quarter 0 0 0 0 0 0
## Air Heads 0 1 0 0 0 0
## Almond Joy 1 0 0 1 0 0
## hard bar pluribus sugarpercent pricepercent winpercent
## 100 Grand 0 1 0 0.732 0.860 66.97173
## 3 Musketeers 0 1 0 0.604 0.511 67.60294
## One dime 0 0 0 0.011 0.116 32.26109
## One quarter 0 0 0 0.011 0.511 46.11650
## Air Heads 0 0 0 0.906 0.511 52.34146
## Almond Joy 0 1 0 0.465 0.767 50.34755Q1. How many different candy types are in this dataset?
dim(candy)## [1] 85 12nrow(candy)## [1] 85Q2. How many fruity candy types are in the dataset?
table(candy$fruity)##
## 0 1
## 47 38The functions dim(), nrow(), table() and sum() may be useful for answering the first 2 questions.
We can find the winpercent value for Twix by using its name to access the corresponding row of the dataset. This is because the dataset has each candy name as rownames (recall that we set this when we imported the original CSV file). For example the code for Twix is:
What is your favorate candy?
candy["Twix", ]$winpercent## [1] 81.64291Q3. What is your favorite candy in the dataset and what is it’s winpercent value?
candy["Swedish Fish",]$winpercent## [1] 54.86111Q4. What is the winpercent value for “Kit Kat”?
candy["Kit Kat",]$winpercent## [1] 76.7686Q5. What is the winpercent value for “Tootsie Roll Snack Bars”?
candy["Tootsie Roll Snack Bars",]$winpercent## [1] 49.6535Side-note: the skimr::skim() function
There is a useful skim() function in the skimr package that can help give you a quick overview of a given dataset. Let’s install this package and try it on our candy data.
library("skimr")
skim(candy)| Name | candy |
| Number of rows | 85 |
| Number of columns | 12 |
| _______________________ | |
| Column type frequency: | |
| numeric | 12 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| chocolate | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| fruity | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| caramel | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| peanutyalmondy | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| nougat | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| crispedricewafer | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| hard | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| bar | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| pluribus | 0 | 1 | 0.52 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| sugarpercent | 0 | 1 | 0.48 | 0.28 | 0.01 | 0.22 | 0.47 | 0.73 | 0.99 | ▇▇▇▇▆ |
| pricepercent | 0 | 1 | 0.47 | 0.29 | 0.01 | 0.26 | 0.47 | 0.65 | 0.98 | ▇▇▇▇▆ |
| winpercent | 0 | 1 | 50.32 | 14.71 | 22.45 | 39.14 | 47.83 | 59.86 | 84.18 | ▃▇▆▅▂ |
Q6. Is there any variable/column that looks to be on a different scale to the majority of the other columns in the dataset?
Q7. What do you think a zero and one represent for the candy$chocolate column? Hint: look at the “Variable type” print out from the skim() function. Most varables (i.e. columns) are on the zero to one scale but not all. Some columns such as chocolate are exclusively either zero or one values.
A good place to start any exploratory analysis is with a histogram. You can do this most easily with the base R function hist(). Alternatively, you can use ggplot() with geom_hist(). Either works well in this case and (as always) its your choice.
Q8. Plot a histogram of winpercent values
hist(candy$winpercent)
Q9. Is the distribution of winpercent values symmetrical? non-symmetrical
Q10. Is the center of the distribution above or below 50%?
summary(candy$winpercent)## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 22.45 39.14 47.83 50.32 59.86 84.18below 50%
Q11. On average is chocolate candy higher or lower ranked than fruity candy?
candy_chocolate <- candy$winpercent[as.logical(candy$chocolate)]
mean(candy_chocolate)## [1] 60.92153candy_fruity <- candy$winpercent[as.logical(candy$fruity)]
mean(candy_fruity)## [1] 44.11974Cho
Q12. Is this difference statistically significant? Hint: The chocolate, fruity, nougat etc. columns indicate if a given candy has this feature (i.e. one if it has nougart, zero if it does not etc.). We can turn these into logical (a.k.a. TRUE/FALSE) values with the as.logical() function. We can then use this logical vector to access the coresponding candy rows (those with TRUE values). For example to get the winpercent values for all nougat contaning candy we can use the code: candy\(winpercent[as.logical(candy\)nougat)]. In addation the functions mean() and t.test() should help you answer the last two questions here.
t.test(candy_chocolate, candy_fruity)##
## Welch Two Sample t-test
##
## data: candy_chocolate and candy_fruity
## t = 6.2582, df = 68.882, p-value = 2.871e-08
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## 11.44563 22.15795
## sample estimates:
## mean of x mean of y
## 60.92153 44.11974# Overall Candy Rankings
Q13. What are the five least liked candy types in this set?
head(candy[order(candy$winpercent),], n=5)## chocolate fruity caramel peanutyalmondy nougat
## Nik L Nip 0 1 0 0 0
## Boston Baked Beans 0 0 0 1 0
## Chiclets 0 1 0 0 0
## Super Bubble 0 1 0 0 0
## Jawbusters 0 1 0 0 0
## crispedricewafer hard bar pluribus sugarpercent pricepercent
## Nik L Nip 0 0 0 1 0.197 0.976
## Boston Baked Beans 0 0 0 1 0.313 0.511
## Chiclets 0 0 0 1 0.046 0.325
## Super Bubble 0 0 0 0 0.162 0.116
## Jawbusters 0 1 0 1 0.093 0.511
## winpercent
## Nik L Nip 22.44534
## Boston Baked Beans 23.41782
## Chiclets 24.52499
## Super Bubble 27.30386
## Jawbusters 28.12744Q14. What are the top 5 all time favorite candy types out of this set?
head(candy[order(candy$winpercent, decreasing = TRUE),], n=5)## chocolate fruity caramel peanutyalmondy nougat
## ReeseÕs Peanut Butter cup 1 0 0 1 0
## ReeseÕs Miniatures 1 0 0 1 0
## Twix 1 0 1 0 0
## Kit Kat 1 0 0 0 0
## Snickers 1 0 1 1 1
## crispedricewafer hard bar pluribus sugarpercent
## ReeseÕs Peanut Butter cup 0 0 0 0 0.720
## ReeseÕs Miniatures 0 0 0 0 0.034
## Twix 1 0 1 0 0.546
## Kit Kat 1 0 1 0 0.313
## Snickers 0 0 1 0 0.546
## pricepercent winpercent
## ReeseÕs Peanut Butter cup 0.651 84.18029
## ReeseÕs Miniatures 0.279 81.86626
## Twix 0.906 81.64291
## Kit Kat 0.511 76.76860
## Snickers 0.651 76.67378To examine more of the dataset in this vain we can make a barplot to visualize the overall rankings. We will use an iterative approach to building a useful visulization by getting a rough starting plot and then refining and adding useful details in a stepwise process.
Q15. Make a first barplot of candy ranking based on winpercent values. HINT: Use the aes(winpercent, rownames(candy)) for your first ggplot like so:
library("ggplot2")ggplot(candy) +
aes(winpercent, rownames(candy)) +
geom_col()
Q16. This is quite ugly, use the reorder() function to get the bars sorted by winpercent?
ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col()
Time to add some useful color Let’s setup a color vector (that signifies candy type) that we can then use for some future plots. We start by making a vector of all black values (one for each candy). Then we overwrite chocolate (for chocolate candy), brown (for candy bars) and red (for fruity candy) values.
my_cols=rep("black", nrow(candy))
my_cols[as.logical(candy$chocolate)] = "chocolate"
my_cols[as.logical(candy$bar)] = "brown"
my_cols[as.logical(candy$fruity)] = "pink"Now let’s try our barplot with these colors. Note that we use fill=my_cols for geom_col(). Experement to see what happens if you use col=mycols.
ggplot(candy) +
aes(winpercent, reorder(rownames(candy),winpercent)) +
geom_col(fill=my_cols) 
Now, for the first time, using this plot we can answer questions like: - Q17. What is the worst ranked chocolate candy? Sixlets - Q18. What is the best ranked fruity candy? Starburst
Taking a look at pricepercent
library(ggrepel)How about a plot of price vs win
ggplot(candy) +
aes(winpercent, pricepercent, label=rownames(candy)) +
geom_point(col=my_cols) +
geom_text_repel(col=my_cols, size=3.3, max.overlaps = 5)## Warning: ggrepel: 50 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
Q19. Which candy type is the highest ranked in terms of winpercent for the least money - i.e. offers the most bang for your buck?
ord <- order(candy$pricepercent, decreasing = FALSE)
head(candy[ord,c(11,12)], n=5 )## pricepercent winpercent
## Tootsie Roll Midgies 0.011 45.73675
## Pixie Sticks 0.023 37.72234
## Dum Dums 0.034 39.46056
## Fruit Chews 0.034 43.08892
## Strawberry bon bons 0.058 34.57899Tootsie Roll Midgies is the best budgeted candy.
Q20. What are the top 5 most expensive candy types in the dataset and of these which is the least popular?
ord <- order(candy$pricepercent, decreasing = TRUE)
head(candy[ord,c(11,12)], n=5 )## pricepercent winpercent
## Nik L Nip 0.976 22.44534
## Nestle Smarties 0.976 37.88719
## Ring pop 0.965 35.29076
## HersheyÕs Krackel 0.918 62.28448
## HersheyÕs Milk Chocolate 0.918 56.49050The most expensive ones are Nik L Nip, and it has one of the worst rating.
Exploring the correlation structure
library(corrplot)## corrplot 0.90 loadedcorrplot 0.90 loaded
cij <- cor(candy)
corrplot(cij)
Q22. Examining this plot what two variables are anti-correlated (i.e. have minus values)? Chocolate and fruity
Q23. Similarly, what two variables are most positively correlated? Chocolate and winpercent
Let’s apply PCA using the prcom() function to our candy dataset remembering to set the scale=TRUE argument.
pca <- prcomp(candy, scale=TRUE)
summary(pca)## Importance of components:
## PC1 PC2 PC3 PC4 PC5 PC6 PC7
## Standard deviation 2.0788 1.1378 1.1092 1.07533 0.9518 0.81923 0.81530
## Proportion of Variance 0.3601 0.1079 0.1025 0.09636 0.0755 0.05593 0.05539
## Cumulative Proportion 0.3601 0.4680 0.5705 0.66688 0.7424 0.79830 0.85369
## PC8 PC9 PC10 PC11 PC12
## Standard deviation 0.74530 0.67824 0.62349 0.43974 0.39760
## Proportion of Variance 0.04629 0.03833 0.03239 0.01611 0.01317
## Cumulative Proportion 0.89998 0.93832 0.97071 0.98683 1.00000Now we can plot our main PCA score plot of PC1 vs PC2.
plot(pca$x[,1:2])
We can change the plotting character and add some color:
plot(pca$x[,1:2], col=my_cols, pch=16)
Make a new data-frame with our PCA results and candy data
my_data <- cbind(candy, pca$x[,1:3])p <- ggplot(my_data) +
aes(x=PC1, y=PC2,
size=winpercent/100,
text=rownames(my_data),
label=rownames(my_data)) +
geom_point(col=my_cols)
p
library(ggrepel)
p + geom_text_repel(size=3.3, col=my_cols, max.overlaps = 7) +
theme(legend.position = "none") +
labs(title="Halloween Candy PCA Space",
subtitle="Colored by type: chocolate bar (dark brown), chocolate other (light brown), fruity (red), other (black)",
caption="Data from 538")## Warning: ggrepel: 39 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
library(plotly)##
## Attaching package: 'plotly'## The following object is masked from 'package:ggplot2':
##
## last_plot## The following object is masked from 'package:stats':
##
## filter## The following object is masked from 'package:graphics':
##
## layoutggplotly(p)Let’s finish by taking a quick look at PCA our loadings. Do these make sense to you? Notice the opposite effects of chocolate and fruity and the similar effects of chocolate and bar (i.e. we already know they are correlated).
par(mar=c(8,4,2,2))
barplot(pca$rotation[,1], las=2, ylab="PC1 Contribution")
Q24. What original variables are picked up strongly by PC1 in the positive direction? Do these make sense to you? Fruity and pluribus.
HINT. pluribus means the candy comes in a bag or box of multiple candies.